Using an out-of-core technique for clustering large data sets

被引：1

作者：

Masciari, E ^{[1
]}

Pizzuti, C ^{[1
]}

Raimondo, G ^{[1
]}

Talia, D ^{[1
]}

机构：

[1] Univ Calabria, DEIS, I-87036 Arcavacata Di Rende, Italy

来源：

12TH INTERNATIONAL WORKSHOP ON DATABASE AND EXPERT SYSTEMS APPLICATIONS, PROCEEDINGS | 2001年

关键词：

D O I：

10.1109/DEXA.2001.953053

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Data mining algorithms generally deal with very large data sets that do not fit in main memory. Therefore, techniques that manage huge data sets need to be developed. Any algorithm that is proposed for mining data should have to account for out-of-core data structures. However, most of the existing algorithms haven't yet addressed this issue. In this paper we describe the implementation of an out-of-core technique for the data analysis of very large data sets with the sequential and parallel version of the clustering algorithm AutoClass. We discuss the out-of-core technique and show performance results in terms of execution time and speed zip.

引用

页码：133 / 137

页数：3

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